Development of a Facebook Addiction Scale

Abstract

The Bergen Facebook Addiction Scale (BFAS), initially a pool of 18 items, three reflecting each of the six core elements of addiction (salience, mood modification, tolerance, withdrawal, conflict, and relapse), was constructed and administered to 423 students together with several other standardized self-report scales (Addictive Tendencies Scale, Online Sociability Scale, Facebook Attitude Scale, NEO-FFI, BIS/BAS scales, and Sleep questions). That item within each of the six addiction elements with the highest corrected item-total correlation was retained in the final scale. The factor structure of the scale was good (RMSEA = .046, CFI = .99) and coefficient alpha was .83. The 3-week test-retest reliability coefficient was .82. The scores converged with scores for other scales of Facebook activity. Also, they were positively related to Neuroticism and Extraversion, and negatively related to Conscientiousness. High scores on the new scale were associated with delayed bedtimes and rising times.

Full text

Psychological Reports, 2012, 110, 2, 501-517. © Psychological Reports 2012
DOI 10.2466/02.09.18.PR0.110.2.501-517 ISSN 0033-2941
DEVELOPMENT OF A FACEBOOK ADDICTION SCALE1, 2
CECILIE SCHOU ANDREASSEN
Department of Psychosocial Science
University of Bergen
The Bergen Clinics Foundation, Norway
TORBJØRN TORSHEIM, GEIR SCOTT BRUNBORG, AND STÅLE PALLESEN
Department of Psychosocial Science
University of Bergen, Norway
Summary.—The Bergen Facebook Addiction Scale (BFAS), initially a pool of
18 items, three reecting each of the six core elements of addiction (salience, mood
modication, tolerance, withdrawal, conict, and relapse), was constructed and
administered to 423 students together with several other standardized self-report
scales (Addictive Tendencies Scale, Online Sociability Scale, Facebook Attitude
Scale, NEO–FFI, BIS/BAS scales, and Sleep questions). That item within each of
the six addiction elements with the highest corrected item-total correlation was re-
tained in the nal scale. The factor structure of the scale was good (RMSEA = .046,
CFI = .99) and coecient alpha was .83. The 3-week test-retest reliability coecient
was .82. The scores converged with scores for other scales of Facebook activity.
Also, they were positively related to Neuroticism and Extraversion, and negatively
related to Conscientiousness. High scores on the new scale were associated with
delayed bedtimes and rising times.
Although pathological gambling is the only behavioral addiction, so
far, to be assigned status as a formal psychiatric disorder, increasing re-
search has been conducted on other potential behavioral addictions, such
as video-game addiction (Fisher, 1994), exercise addiction (Adams & Kirk-
by, 2002), mobile-phone addiction (Choliz, 2010), online sex addiction
(Griths, 2012), shopping addiction (Clark & Calleja, 2008), workaholism
(Andreassen, Hetland, & Pallesen, 2010), and Internet addiction (Young,
1996; Beard, 2005). With regard to Internet addiction, it has been ques-
tioned whether people become addicted to the platform or to the content
of the Internet (Griths, 1999). Young (2009) argued that Internet addicts
become addicted to dierent aspects of online use. She dierentiates be-
tween three subtypes of Internet addicts: excessive gaming, online sexual
pre-occupation, and e-mailing/texting (Young, 2009). Social networks are
one type of online activity in which e-mailing/texting has been predomi-
nant. Among social networks, Facebook is by far the most popular, with
1Address correspondence to Cecilie Schou Andreassen, Department of Psychosocial Science,
University of Bergen, Christiesgt. 12, 5015 Bergen, Norway or e-mail (cecilie.andreassen@
psych.uib.no).
2The authors thank Annika Hessen and Mathilde Døving for their help with data collection.

C. S. andreaSSen, et al.
502
over 600 million users worldwide (Carlson, 2011). In one study, students
classied as Internet-addicted used the Internet more for social functions
than students considered non-addicted (Kesici & Sahin, 2009).
A recently published review article on social networking and addic-
tion suggests that social network sites are predominantly used for main-
tenance of established oine networks which, for many, are important
in terms of academic and professional opportunities. The maintenance of
such networks and staying connected are assumed to function as an at-
traction factor, which might explain why some individuals use social net-
work sites excessively (Kuss & Griths, 2011). Researchers have linked
Facebook use to specic individual characteristics. People scoring high on
narcissism tend to be more active on social network sites, as social network
sites provide an opportunity to present oneself in a favorable way in line
with one’s ideal self (Buardi & Campbell, 2008; Mehdizadeh, 2010). Oth-
er studies have focused on the ve-factor model of personality, in which
personality assessment is based on ve main dimensions of Extraversion
(e.g., being outgoing, talkative), Agreeableness (e.g., being sympathetic
and warm), Conscientiousness (e.g., being organized and prompt), Neu-
roticism (e.g., being nervous and moody), and Openness to experience
(e.g., being creative and intellectually oriented) (Wiggins, 1996). Some pre-
vious researchers have reported extraversion as positively related to In-
ternet use in general (Yang & Lester, 2003). In addiction to social media,
addictive tendencies have been reported to be positively related to Extra-
version and negatively related to Conscientiousness (Wilson, Fornasier,
& White, 2010). Also, Correa, Hinsley, and de Zuniga (2010) reported that
Extraversion, Neuroticism, and Openness to experience were all positive-
ly associated with frequency of social media use. It has been suggested
that extroverts use social media for social enhancement, whereas intro-
verts use it for social compensation, each of which appears to be associ-
ated with elevated use (Kuss & Griths, 2011). People who score low on
Conscientiousness are assumed to use social media as a way of procrasti-
nating, hence, Conscientiousness is assumed to be negatively associated
with social media use (Wilson, et al., 2010). Neuroticism is assumed to be
positively related to use of social media as it may be a way of seeking sup-
port. In addition, social media gives people with high scores on Neuroti-
cism more time for contemplation before acting compared to face-to-face
interactions (Ehrenberg, Juckes, White, & Walsh, 2008; Ross, Orr, Sisic, Ar-
seneault, Simmering, & Orr, 2009; Correa, et al., 2010).
Addictive behaviors may also be related to individual dierences in
sensitivity to reward and punishment. According to Gray (1982), one sys-
tem, the behavioral inhibition system (BIS), is associated with sensitiv-
ity to conditioned punishment, whereas another system, the behavioral

Bergen FaceBook addiction Scale 503
approach system (BAS), is associated with sensitivity to conditioned re-
ward. These two systems can be measured using self-report scales, one
scale for BIS, and three subscales for BAS: Reward Responsiveness, Drive,
and Fun-seeking (Carver & White, 1994). It has been suggested that high
behavioral approach system (BAS) sensitivity predisposes to conditions
that are characterized by high engagement in approach behaviors, such as
alcohol and drug abuse (Franken, Muris, & Georgieva, 2006). In one study,
Internet addiction was positively related to scores on the BIS scale and the
BAS Fun-seeking subscale (Yen, Ko, Yen, Chen, & Chen, 2009).
Poor and short sleep has, in several studies, been linked to impaired
academic performance (Dewald, Meijer, Oort, Kerkhof, & Bögels, 2010).
Recently, studies have shown that excessive use of electronic media may
delay bedtimes and rising times (Suganuma, Kikuchi, Yanagi, Yamamu-
ra, Morishima, Adachi, et al., 2007; Brunborg, Mentzoni, Molde, Myrseth,
Skouverøe, Bjorvatn, et al., 2011). These researchers, however, did not con-
sider the content of computer and mobile-phone use. Since Facebook has
become one of the most used sites on the Internet, and since poor sleep
may be detrimental to the academic performance of students, investiga-
tion of whether Facebook addiction, in particular, may be directly associ-
ated with sleep habits would be of interest.
In relation to assessing Facebook addiction, Wilson, et al. (2010) pre-
viously developed the Addictive Tendencies Scale, which has three items
reecting salience, loss of control, and withdrawal. Although these three
aspects have been central in thinking about addictions, in the literature,
addiction has involved six core components: (1) salience—the activity
dominates thinking and behavior; (2) mood modication—the activity
modies/improves mood; (3) tolerance—increasing amounts of the activ-
ity are required to achieve previous eects; (4) withdrawal—the occur-
rence of unpleasant feelings when the activity is discontinued or sudden-
ly reduced; (5) conict—the activity causes conicts in relationships, in
work/education, and other activities; and (6) relapse—a tendency to re-
vert to earlier patterns of the activity after abstinence or control (Brown,
1993; Griths, 1996, 2005). In line with this, studies have shown that so-
cial-network site use can lead to a variety of negative consequences such
as decrease in real-life communities, worsening of academic performance,
and relationship problems (Kuss & Griths, 2011).
As addiction to Facebook may be a specic form of Internet addiction,
and since the use of Facebook is increasing very rapidly, there is a need for
a psychometrically sound procedure for assessing a possible addiction.
Against this background, a Facebook addiction scale (the Bergen Face-
book Addiction Scale) with as few items as possible (one reecting each
of the six above-mentioned elements of addiction, ensuring its content va-

C. S. andreaSSen, et al.
504
lidity) was constructed. A new Facebook addiction scale should correlate
highly with measures of similar constructs (convergent validity) and less
with measures of more divergent or unrelated constructs (discriminant
validity) (Cozby, 2009).
The following hypotheses were tested: (1) the Bergen Facebook Ad-
diction Scale (BFAS) will have a unidimensional factor structure with high
factor loadings for all items, t indexes [root mean square error of ap-
proximation (RMSEA) and comparative t index (CFI)] showing good t
with the data and factor loading invariance across sexes; (2) the 3-week
test-retest reliability will be high (r > .75); (3) ratings on the BFAS will cor-
relate positively and signicantly with scores on other scales of Facebook
use (the Addictive Tendencies Scale, as well as scales measuring Facebook
attitudes and use, respectively); (4) ratings on the scale will be positively
related to those on Neuroticism and Extraversion and negatively related
to those on Conscientiousness; (5) ratings on the scale will be positively
associated with ratings on the BIS scale and with those on the BAS Fun-
seeking subscale; and (6) the scores on the BFAS will correlate positively
and signicantly with bedtimes and rising times.
Method
Participants
The sample comprised a total of 423 college students (227 women).
Their mean age was 22.0 yr. (SD = 4.0). A subsample (n = 153, 118 women,
35 men) of these were present at a later lecture and were used for test-re-
test of the BFAS. The mean age of the retest sample was 21.3 yr. (SD = 4.1).
Procedure
Potential items to be included in the Facebook addiction scale were
constructed for the six basic components of addiction proposed by Brown
(1993) and Griths (1996). Three items for each component were chosen.
Wording was similar to that used in the diagnostic criteria for pathologi-
cal gambling (American Psychiatric Association, 2000) and in the Game
Addiction Scale (Lemmens, Valkenburg, & Peter, 2009). These items were
included in a self-report questionnaire with additional questions about
demography, Facebook activity, personality, and sleep habits. The ques-
tionnaire was distributed at undergraduate lectures in psychology at the
University of Bergen, Norway, to engineering students at Bergen College,
and students at the Royal Norwegian Naval Academy during the spring
of 2011. Questionnaire completion took approximately 20 minutes. No
monetary or other material incentives were oered in return for partici-
pation. Response rate was 95%. Questionnaires were coded with unique
numbers that students were asked to note and keep for later re-adminis-
tration of some of the questions. They were not informed which questions

Bergen FaceBook addiction Scale 505
would be re-administered. Three weeks after the rst questionnaire was
administered, the 18 items were re-administered to 36.2% of these under-
graduates. Participants were asked to write the unique number code on
the questionnaire for administrative use in identifying which students an-
swered questions twice.
Measures
The Bergen Facebook Addiction Scale (BFAS).—This scale comprised 18
items, three for each of the six core features of addiction: salience, mood
modication, tolerance, withdrawal, conict, and relapse. Each item is
scored on a 5-point scale using anchors of 1: Very rarely and 5: Very often.
Higher scores indicate greater Facebook addiction. All 18 original items
are listed in Appendix A (p. 516). Cronbach alpha was .83 in this sample.
The Facebook Attitude Scale.—This scale has six items for assessing atti-
tudes toward Facebook. Each item is rated on a 5-point scale with anchors
of 1: Strongly disagree and 5: Strongly agree. Higher scores then reect
positive attitudes toward Facebook (Ellison, Steineld, & Lampe, 2007).
Internal consistency (Cronbach alpha) was .82 in the present study.
The Online Sociability Scale.—This scale comprises ve items, each per-
taining to frequencies of dierent uses of Facebook (e.g., comment on oth-
er photographs, sending private messages). Scores are ratings on a 9-point
scale using anchors of 1: Less than once per year and 9: More than once
daily (Ross, et al., 2009). High ratings reect high frequency of Facebook
use. Cronbach alpha of this scale was .63 in the present study.
The Addictive Tendencies Scale.—The scale (Wilson, et al., 2010) has
three items representing salience to, loss of control of, and withdrawal
from Facebook use. Each item is rated on a 7-point scale, with anchors of 1:
Strongly disagree and 7: Strongly agree. High ratings indicate high addic-
tive tendencies. These items were from previous scales assessing addictive
tendencies in use of text messages and instant messaging services (Ehren-
berg, et al., 2008). Cronbach alpha of this scale was .72 in the present study.
The NEO–Five Factor Inventory (NEO–FFI).—This is a short 60-item
version of the NEO Personality Inventory–Revised, which provides a
brief, comprehensive measure of the domains of the ve-factor model of
personality: Neuroticism, Extraversion, Openness, Agreeableness, and
Conscientiousness. Each subscale has 12 items rated on a 5-point scale
(Costa & McCrae, 1992). Values of Cronbach alpha for the scales in the
present study were .89 (Neuroticism), .80 (Extraversion), .74 (Openness),
.71 (Agreeableness), and .82 (Conscientiousness).
The BIS/BAS scales.—The BIS scale assesses behavioral inhibition us-
ing seven items. Focus is on measuring predisposition to avoid threaten-
ing or punishing stimuli. The BAS scale of 13 items assesses predispo-
sition to approach appetitive stimuli. There are three subscales: Reward

C. S. andreaSSen, et al.
506
responsiveness (BAS–RR), Drive (BAS–D), and Fun-seeking (BAS–FS).
Participants indicate how much they agree with statements on a 4-point
scale using anchors of 1: Very false for me and 4: Very true for me (Carver
& White, 1994). Internal consistencies (Cronbach alpha) of the scales in the
present study were for BIS .79, BAS–RR .58, BAS–D .78, and BAS–FS .58.
Sleep questions.—Content concerned habitual bedtimes and rising
times on weekdays and weekends, respectively. These questions have
been used in previous research (Pallesen, Saxvig, Molde, Sørensen, Wil-
helmsen-Langeland, & Bjorvatn, 2011) and seem to reect the circadian
rhythm of the participant (Bjorvatn & Pallesen, 2009). High numbers/
scores indicate late bedtimes and rising times.
Analysis
Item selection.—Of the three items within each of the six core addiction
elements, the one with the highest item-total correlation with the sum of
ratings for all the other 17 items was retained. These analyses were con-
ducted with PASW statistics, Version 18.0.
Factor analysis.—A one-factor solution was expected and investigat-
ed. The error term of each indicator was assumed to be uncorrelated with
each of the others. The CFI and the RMSEA were used as t indexes. As
a rule of thumb, for a model with acceptable t to the data, these index-
es should be < .08 and > .90, respectively, whereas the corresponding val-
ues for a good t would be < .06 and > .95, respectively (Hu & Bentler,
1999). Missing data were excluded pairwise. Pearson correlations among
all items are shown in Appendix B (p. 517).
Correlations and regression analysis.—All other analyses were conduct-
ed using PASW, Version 18.0, unless explicitly stated otherwise. To in-
vestigate the test-retest reliability of responses to the BFAS, the Pearson
product-moment correlation coecient between ratings from the rst ad-
ministration and the re-administration of the scale was calculated. Score-
Rel CI software was used to calculate the 95%CI for the test-retest corre-
lation (Barnette, 2005). Pearson product-moment correlation coecients
were calculated to investigate the convergent validity between scores on
the BFAS and on the Facebook Attitude Scale, the Online Sociability Scale,
and the Addictive Tendencies Scale. A hierarchical multiple regression
analysis was conducted to assess how ratings on the BFAS were related to
the ve-factor model of personality as well as to measures of the behavior-
al inhibition system and of the behavioral activation system. Participants’
age and sex were entered in the rst step. In the second step, the ratings
for the ve subscales (Neuroticism, Extraversion, Openness, Agreeable-
ness, and Conscientiousness) of the NEO Five-Factor Inventory were en-
tered, as well as ratings from the four subscales of the BIS/BAS scales (the
Behavioral Inhibition Scale and the Behavioral Approach Scales: Reward

Bergen FaceBook addiction Scale 507
Responsiveness, Drive, and Fun-seeking). Preliminary analyses were con-
ducted to ensure there was no violation of the assumption of normality,
linearity, multicollinearity, and homoscedasticity. Pearson product-mo-
ment correlations were calculated for the relations of the scores on the
BFAS with responses to the sleep questions.
Results
Factor Structure
The corrected item-total correlation coecients for all initial 18 items
are presented in Appendix A. The corrected item-total correlation coe-
cient of each of the six core addiction elements retained ranged from .60
to .73 (see Appendix A, p. 516). The conrmatory factor analysis showed
that all standardized loadings of the six indicators on the one-factor solu-
tion (χ2/df = 1.84, p > .05) were above .50 (range = .59 to .80; see Fig. 1). The
RMSEA of the model was 0.05 (90%CI = 0.00, 0.08) and the CFI was .99.
Cronbach alpha for the BFAS was .83 for the whole sample and .83 for the
retest subsample. Comparing a model with no constraints to a model with
constraints on the factor loadings across sexes indicated factor loading in-
variance (Δχ2 = 8.86, df = 5, p > .05).
Test-retest Reliability
The 3-week test-retest correlation coecient (n = 153) was .82 (p < .01;
95%CI = .75, .86).
Convergent and Discriminative Validity
Table 1 shows the Pearson product-moment correlation coecients
among ratings on the BFAS, the Addictive Tendencies Scale, the Facebook
Fig. 1. Factor structure and standardized loadings of items in the Bergen Facebook
Addiction Scale
e1
Faceb ook
Addiction
e5
e7
e11
e13
e16
.37
.61
.80
.59
.67
.69
.69
.64
.35
.44
.47
.47
BFAS 1
BFAS 5
BFAS 7
BF AS 11
BFAS 13
BFAS 16

C. S. andreaSSen, et al.
508
Attitudes Scale, and the Online Sociability Scale. The BFAS correlated pos-
itively and signicantly with all of these scales. The correlation coecient
between ratings on the BFAS and on the Addictive Tendencies Scale was
statistically signicantly higher than the correlation coecient between
the BFAS and the Facebook Attitudes Scale (Δr = .11, t = 3.84, df = 394,
p < .01) and between the BFAS and the Online Sociability Scale (Δr = .31,
t = 8.22, df = 394, p < .01).
Relations with Five-factor Model of Personality and Reward Sensitivity
In Table 2 is a summary of results from the regression analysis predict-
ing scores on the BFAS. Participants’ age and sex were entered at Step 1,
TABLE 1
PeaRson PRoduct-MoMent coRRelation coeFFicients Between scoRes on
BeRgen FaceBook addiction scale, addictive tendencies scale,
FaceBook attitudes scale, and online sociaBility scale
Scale Addictive
Tendencies Scale Facebook
Attitudes Scale
Online Sociability
Scale
rnrnrn
Bergen Facebook Addiction Scale .69 403 .58 397 .37 400
Addictive Tendencies Scale .69 397 .45 400
Facebook Attitudes Scale .51 395
Note.—All ps < .01.
TABLE 2
suMMaRy oF hieRaRchical RegRession analysis FoR vaRiaBles PRedicting
scoRes on BeRgen FaceBook addiction scale (N = 386)
Predictor βtΔR2
Step 1 .123
Sex (male = 1, female = 2) .30†6.22
Age −.12* −2.48
Step 2 .145
Sex (male = 1, female = 2) .25†4.69
Age −.08 −1.70
Neuroticism .25†4.01
Extraversion .22†3.79
Openness to experience −.05 −1.15
Agreeableness −.04 −0.79
Conscientiousness −.23†−4.47
Behavioral Inhibition (BIS) .13 1.95
Behavioral Approach–Reward responsiveness (BAS–RR) .03 0.54
Behavioral Approach–Drive (BAS–D) .05 0.82
Behavioral Approach–Fun-seeking (BAS–FS) −.11* − 2.06
*p < .05. †p < .01.

Bergen FaceBook addiction Scale 509
explaining 12.3% of the variance. After entering the scores for the ve sub-
scales of the NEO-Five Factor Inventory and the scores for the BIS/BAS
Scales, the total variance explained by the model as a whole was 26.8%
(F11,375 = 12.5, p < .01). The personality variables entered at Step 2 explained
an additional 14.5% of the variance in scores on the BFAS, after control-
ling for age and sex (R2 change = .15, F change9,375 = 8.3, p < .01). In the nal
model, scores on the BFAS were statistically signicantly and positively
related to sex (coded men = 1, women = 2). Neuroticism and Extraversion
were statistically signicantly and positively related to the ratings on the
BFAS, whereas Conscientiousness was negatively related. Ratings on the
BAS Fun-seeking subscale were negatively and statistically signicantly
related to ratings on the BFAS. The ve signicant variables explained a
total of 23.7% of the variance.
Facebook Addiction and Sleep Parameters
Table 3 shows the Pearson product-moment correlation coecients
by ratings on the BFAS with bedtimes and rising times on weekdays and
weekends, respectively. Values were all statistically and positively signi-
cant.
TABLE 3
PeaRson PRoduct-MoMent coRRelation coeFFicients Between scoRes on BeRgen
FaceBook addiction scale and FouR sleeP PaRaMeteRs (N = 403 to 423)
Bedtime
Weekdays
Bedtime
Weekends
Rising Time
Weekdays
Rising Time
Weekends
Bergen Facebook Addiction Scale .11 .17 .26 .17
Bedtime weekdays .53 .47 .46
Bedtime weekends .31 .55
Rising time weekdays .46
Note.—r = .11, p < .05; .17 ≤ r ≤.55, p < .01.
discussion
The rst hypothesis was that the BFAS would have a unidimensional
factor structure. All loadings were above .50. The CFI was above .95 and
the RMSEA was below .06, which both indicate a good t (Hu & Bentler,
1999). Conrmatory factor analysis, as used in the present study, seems
to aord a stricter interpretation of unidimensionality than can be pro-
vided by more traditional methods (Gerbing & Anderson, 1988). In addi-
tion, the results showed that there was no dierence between males and
females in terms of the factor loadings of the model, hence factor loading
invariance across the sexes was demonstrated. Thus, the rst hypothesis
was supported.
The second hypothesis concerned the test-retest reliability of the scale,

C. S. andreaSSen, et al.
510
which in this case was .82 for re-administration after 3 weeks. The lower
end of the 95%CI for the test-retest correlation coecient was also with-
in the expected value (> .75). Thus, the second hypothesis was also sup-
ported.
The third hypothesis implied that the scores for the BFAS would be
highly correlated, specically with Facebook measures of addictive ten-
dencies, attitudes, and online sociability. As the scores were positively cor-
related with all the other Facebook scales and were related signicantly
higher with the Addictive Tendencies scale than with scores on the Face-
book Attitudes Scale and the Online Sociability Scale, one may infer the
BFAS showed good convergent and discriminative validity (Carmines &
Zeller, 1979). Scores on the BFAS correlated higher with scores reecting
problematic Facebook use than scores reecting general use and general
attitudes toward Facebook. This indicates that the BFAS primarily mea-
sures misuse and not general use of Facebook, therefore, Hypothesis 3
was supported.
Before the ndings concerning Hypotheses 4 and 5 are discussed,
some comments regarding demographic variables which were signi-
cantly related to BFAS are warranted. In the regression analysis, wom-
en had higher scores than men on the BFAS, a nding counter to prior
sex dierences related to other behavioral addictions, such as patholog-
ical gambling (Molde, Pallesen, Bartone, Hystad, & Johnsen, 2009) and
video-game addiction (Mentzoni, Brunborg, Molde, Myrseth, Skouverøe,
Hetland, et al., 2011). However, other researchers have reported women to
have higher frequency than men of other behavioral addictions, such as
mobile-phone addiction (Takao, Takahashi, & Kitamura, 2009). This may
allow the inference that men are more prone to become addicted to soli-
tary behaviors, whereas women tend to score higher on measures of be-
havioral addiction involving social interaction.
Hypothesis 4 implied that ratings on BFAS would be positively re-
lated to those on Neuroticism and Extraversion, and negatively related
to those on Conscientiousness. Ratings on the Neuroticism subscale were
positively related to scores on the BFAS, consistent with studies of oth-
er behavioral addictions (Myrseth, Pallesen, Molde, Johnsen, & Lorvik,
2009), including Internet addiction (Tsai, Cheng, Yeh, Shih, Chen, Yang,
et al., 2009). In relation to social media, it has been suggested that anx-
ious people may use social media to obtain support and company (Cor-
rea, et al., 2010). A further hypothesis is that shy and anxious people may
prefer to interact on the web rather than face-to-face, since the former al-
lows more time for planning and rehearsal than the latter (Ehrenberg, et
al., 2008). The hypothesis concerning Neuroticism was supported. Extra-
version was also positively correlated with scores on the BFAS, which is

Bergen FaceBook addiction Scale 511
in line with Ross, et al. (2009) who suggested that those scoring high on
extraversion do not use Facebook as a substitute for social interaction but
rather as an additional way of expressing themselves. Conscientiousness
was expected to be negatively associated with scores on the BFAS, and this
was supported by the data. This nding is in line with studies on heavy
Facebook use (Wilson, et al., 2010) as well as with studies on Internet ad-
diction (Gnisci, Perugini, Pedone, & Di Conza, 2010). This suggests people
with high scores on this trait give less priority to activities such as Face-
book in order to fulll other obligations and meet deadlines for tasks they
have undertaken.
Hypothesis 5 stated that ratings on the BFAS would be positively as-
sociated with ratings on the Behavioral Approach (BAS) subscales. The
only signicant nding concerning the BIS/BAS scale was that the scores
of the BAS Fun-seeking subscale were negatively related to the scores on
the BFAS. This result contradicted the hypothesis, and was contrary to
ndings from a previous study that showed that the scores on this sub-
scale were positively related to Internet addiction (Yen, et al., 2009). The
authors do not have any clear-cut explanation for this unexpected nd-
ing, but one reason could be that people who score high on Fun-seeking
may regard Facebook as “old news,” which does not provide much fun
and novelty.
The nal and sixth hypothesis was that the scores on the BFAS would
be positively related to bedtimes and rising times on both weekdays and
weekends. This hypothesis was supported, which indicates that heavy
Facebook use may interfere with going to bed, and as such, leads to a post-
ponement of both bedtimes and rising times. This interpretation is in line
with previous studies showing that people who use computers in their
bedrooms and/or late in the evening typically have a delayed sleep-wake
rhythm (Suganuma, et al., 2007; Brunborg, et al., 2011).
In terms of limitations it should be noted that the BFAS, so far, only
has been investigated in a student sample. Thus, further studies investi-
gating the psychometric properties of the BFAS in other populations are
warranted. Some of the scales used for validation of the BFAS in the pres-
ent study had low internal consistency, which may have caused underes-
timation of relationships between concepts. The wording of several items
of the BFAS may seem strikingly similar to scales assessing other behav-
ioral addictions, such as the Exercise Addiction Inventory (Terry, Szabo, &
Griths, 2004). This similarity does not reect plagiarism, but the fact that
the scales were based on the same basic addiction criteria (Brown, 1993;
Griths, 1996, 2005).
The authors conclude that, as a new scale for measuring Facebook
addiction, the BFAS has acceptable psychometric properties in terms of

C. S. andreaSSen, et al.
512
internal consistency, factor structure, and reliability, as well as in rela-
tion to content and convergent and discriminative validity. As expected,
the scores on the BFAS relate to specic factors (Neuroticism, Extraver-
sion, and Conscientiousness) in the ve-factor model of personality. The
relationship between the scores on the BFAS and scores on measures of
reward and punishment sensitivity was not as expected, however. The
scores on the BFAS were related to sleep in such a way that higher scores
on the BFAS were associated with later bedtimes and rising times.
The authors suggest that the BFAS can be used in epidemiological as
well as clinical settings. The present study did not examine specic cuto
scores for a categorization of problems with Facebook addiction. Howev-
er, in line with studies assessing other behavioral addictions (Lemmens,
et al., 2009), a liberal approach would entail the use of a polythetic scoring
scheme (e.g., scoring 3 or above on at least four of the six items), whereas
a more conservative approach could be to use a monothetic scoring key
(e.g., scoring 3 or above on all six items). The usefulness of the proposed
cuto value for categorization of Facebook addiction should be pursued
in future studies. The authors would like to point out, for most addictions,
a categorization (or tentative diagnosis) is normally made when the per-
son fulls a given number (e.g., ve of 10 for pathological gambling, three
of seven for substance dependence) of criteria (American Psychiatric As-
sociation, 2000).
REFERENCES
adaMs, J., & kiRkBy, R. J. (2002) Excessive exercise as an addiction: a review. Addiction
Research & Theory, 10, 415-437.
aMeRican PsychiatRic association. (2000) Diagnostic and statistical manual of mental dis-
orders. (4th ed., text rev.) Washington, DC: Author.
andReassen, c. s., hetland, J., & Pallesen, s. (2010) The relationship between “worka-
holism,” basic needs satisfaction at work and personality. European Journal of Per-
sonality, 24, 3-17.
BaRnette, J. J. (2005) ScoreRel CI: an excel program for computing condence inter-
vals for commonly used score reliability coecients. Educational and Psychological
Measurement, 65, 980-983.
BeaRd, k. w. (2005) Internet addiction: a review of current assessment techniques and
potential assessment questions. Cyberpsychology & Behavior, 8, 7-14.
BJoRvatn, B., & Pallesen, s. (2009) A practical approach to circadian rhythm sleep dis-
orders. Sleep Medicine Reviews, 13, 47-60.
BRown, R. i. F. (1993) Some contributions of the study of gambling to the study of
other addictions. In W. R. Eadington & J. Cornelius (Eds.), Gambling behavior and
problem gambling. Reno, NV: Univer. of Nevada Press. Pp. 341-372.
BRunBoRg, g. s., Mentzoni, R. a., Molde, h., MyRseth, h., skouveRøe, k. J. M., BJoRvatn,
B., & Pallesen, s. (2011) The relationship between media use in the bedroom,
sleep habits, and symptoms of insomnia. Journal of Sleep Research, 20, 569-575.

Bergen FaceBook addiction Scale 513
BuFFaRdi, e. l., & caMPBell, w. k. (2008) Narcissism and social networking web sites.
Personality and Social Psychology Bulletin, 34, 1303-1314.
caRlson, n. (2011) Goldman to clients: Facebook has 600 million users. Business In-
sider, January 5. Retrieved from http://www.businessinsider.com/.
caRMines, e. g., & zelleR, R. a. (1979) Reliability and validity assessment. Beverly Hills,
CA: Sage.
caRveR, c. s., & white, t. l. (1994) Behavioral inhibition, behavioral activation, and
aective responses to impending reward and punishment: the BIS/BAS scales.
Journal of Personality and Social Psychology, 67, 319-333.
choliz, M. (2010) Mobile phone addiction: a point of issue. Addiction, 105, 373-374.
claRk, M., & calleJa, k. (2008) Shopping addiction: a preliminary investigation
among Maltese university students. Addiction Research & Theory, 16, 633-649.
coRRea, t., hinsley, a. w., & de zuniga, h. g. (2010) Who interacts on the Web? The
intersection of users’ personality and social media use. Computers in Human Be-
havior, 26, 247-253.
costa, P. t., JR., & MccRae, R. R. (1992) Revised NEO Personality Inventory (NEO-PI–R)
and NEO Five-Factor Inventory (NEO–FFI), professional manual. Odessa, FL: Psycho-
logical Assessment Resources.
cozBy, P. c. (2009) Methods in behavioral research. (10th ed.) Boston: McGraw-Hill.
dewald, J. F., MeiJeR, a. M., ooRt, F. J., keRkhoF, g. a., & Bögels, s. M. (2010) The inu-
ence of sleep quality, sleep duration and sleepiness on school performance in chil-
dren and adolescents: a meta-analytic review. Sleep Medicine Reviews, 14, 179-189.
ehRenBeRg, a., Juckes, s., white, k. M., & walsh, s. P. (2008) Personality and self-es-
teem as predictors of young people’s technology use. Cyberpsychology & Behavior,
11, 739-741.
ellison, n. B., steinField, c., & laMPe, c. (2007) The benets of Facebook friends: so-
cial capital and college students’ use of online social network sites. Journal of Com-
puter-Mediated Communication, 12, Article 1. (Retireved from: http://jcmc.indiana.
edu/vol12/issue4/ellison.html)
FisheR, s. (1994) Identifying video game addiction in children and adolescents. Addic-
tive Behaviors, 19, 545-553.
FRanken, i. h. a., MuRis, P., & geoRgieva, i. (2006) Gray’s model of personality and
addiction. Addictive Behaviors, 31, 399-403.
geRBing, d. a., & andeRson, J. c. (1988) An updated paradigm for scale development
incorporating unidimensionality and its assessment. Journal of Marketing Research,
25, 186-192.
gnisci, a., PeRugini, M., Pedone, R., & di conza, a. (2010) Construct validation of the
use, abuse and dependence on the Internet inventory. Computers in Human Behav-
ior, 27, 240-247.
gRay, J. a. (1982) The neuropsychology of anxiety: enquiry into the functions of the septohip-
pocampal system. Oxford: Oxford Univer. Press.
gRiFFiths, M. d. (1996) Nicotine, tobacco and addiction. Nature, 384, 18.
gRiFFiths, M. d. (1999) Internet addiction: fact or ction? Psychologist, 12, 246-250.
gRiFFiths, M. d. (2005) A “components” model of addiction within a biopsychosocial
framework. Journal of Substance Use, 10, 191-197.
gRiFFiths, M. d. (2012) Internet sex addiction: a review of empirical research. Addiction
Theory and Research, 20, 111-124.

C. S. andreaSSen, et al.
514
hu, l. t., & BentleR, P. M. (1999) Cuto criteria for t indexes in covariance structure
analysis: conventional criteria versus new alternatives. Structural Equation Model-
ing, 6, 1-55.
kesici, s., & sahin, i. (2009) A comparative study of uses of the Internet among college
students with and without Internet addiction. Psychological Reports, 105, 1103-1112.
kuss, d. J., & gRiFFiths, M. d. (2011) Addiction to social networks on the Internet: a
literature review of empirical research. International Journal of Environmental and
Public Health, 8, 3528-3552.
leMMens, J. s., valkenBuRg, P. M., & PeteR, J. (2009) Development and validation of a
game addiction scale for adolescents. Media Psychology, 12, 77-95.
Mehdizadeh, s. (2010) Self-presentation 2.0: narcissism and self-esteem on Facebook.
Cyberpsychology, Behavior, and Social Networking, 13, 357-364.
Mentzoni, R. a., BRunBoRg, g. s., Molde, h., MyRseth, h., skouveRøe, k. J. M., hetland,
J., & Pallesen, s. (2011) Problematic video game use, estimated prevalence, and
associations with mental and physical health. Cyberpsychology, Behavior, and Social
Networking, 14, 591-596.
Molde, h., Pallesen, s., BaRtone, P., hystad, s., & Johnsen, B. h. (2009) Prevalence and
correlates of gambling among 16- to 19-year-old adolescents in Norway. Scandina-
vian Journal of Psychology, 50, 55-64.
MyRseth, h., Pallesen, s., Molde, h., Johnsen, B. h., & loRvik, i. M. (2009) Personality
factors as predictors of pathological gambling. Personality and Individual Dier-
ences, 47, 933-937.
Pallesen, s., saxvig, i. w., Molde, h., søRensen, e., wilhelMsen-langeland, a., & BJoR-
vatn, B. (2011) Brief report: behaviorally induced insucient sleep syndrome in
older adolescents: prevalence and correlates. Journal of Adolescence, 34, 391-395.
Ross, c., oRR, e. s., sisic, M., aRseneault, J. M., siMMeRing, M. g., & oRR, R. R. (2009) Per-
sonality and motivations associated with Facebook use. Computers in Human Be-
havior, 25, 578-586.
suganuMa, n., kikuchi, t., yanagi, k., yaMaMuRa, s., MoRishiMa, h., adachi, h., ku-
Manogo, t., MikaMi, a., sugita, y., & takeda, M. (2007) Using electronic media
before sleep can curtail sleep time and result in self-perceived insucient sleep.
Sleep and Biological Rhythms, 5, 204-214.
takao, M., takahashi, s., & kitaMuRa, M. (2009) Addictive personality and problem-
atic mobile phone use. Cyberpsychology & Behavior, 12, 501-507.
teRRy, a., szaBo, a., & gRiFFiths, M. d. (2004) The Exercise Addiction Inventory: a new
brief screening tool. Addiction Research and Theory, 12, 489-499.
tsai, h. F., cheng, s. h., yeh, t. l., shih, c. c., chen, k. c., yang, y. c., & yang, y. k.
(2009) The risk factors of Internet addiction: a survey of university freshmen.
Psychiatry Research, 167, 294-299.
wiggins, J. s. (1996) The ve-factor model of personality: theoretical perspectives. New York:
Guilford.
wilson, k., FoRnasieR, s., & white, k. M. (2010) Psychological predictors of young
adults’ use of social networking sites. Cyberpsychology, Behavior, and Social Net-
working, 13, 173-177.
yang, B. J., & lesteR, d. (2003) National character and Internet use. Psychological Re-
ports, 93, 940.
yen, J. y., ko, c. h., yen, c. F., chen, c. s., & chen, c. c. (2009) The association be-
tween harmful alcohol use and Internet addiction among college students: com-
parison of personality. Psychiatry and Clinical Neurosciences, 63, 218-224.

Bergen FaceBook addiction Scale 515
young, k. s. (1996) Psychology of computer use: addictive use of the Internet, a case
that breaks the stereotype. Psychological Reports, 79, 899-902.
young, k. s. (2009) Internet addiction: diagnosis and treatment consideration. Journal
of Contemporary Psychotherapy, 39, 241-246.
Accepted February 16, 2012.

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APPENDIX A
the BeRgen FaceBook addiction scale:
iteMs and inteRcoRRelations oF Ratings
How often during the last year have you . . .
Item-total
Correlation
Salience
BFAS1* Spent a lot of time thinking about Facebook or planned use of
Facebook? .61
BFAS2 Thought about how you could free more time to spend on Face-
book? .42
BFAS3 Thought a lot about what has happened on Facebook recently? .55
Tolerance
BFAS4 Spent more time on Facebook than initially intended? .68
BFAS5* Felt an urge to use Facebook more and more? .73
BFAS6 Felt that you had to use Facebook more and more in order to get
the same pleasure from it? .57
Mood modication
BFAS7* Used Facebook in order to forget about personal problems? .60
BFAS8 Used Facebook to reduce feelings of guilt, anxiety, helplessness,
and depression? .55
BFAS9 Used Facebook in order to reduce restlessness? .52
Relapse
BFAS10 Experienced that others have told you to reduce your use of Face-
book but not listened to them? .61
BFAS11* Tried to cut down on the use of Facebook without success? .68
BFAS12 Decided to use Facebook less frequently, but not managed to do so? .62
Withdrawal
BFAS13* Become restless or troubled if you have been prohibited from using
Facebook? .69
BFAS14 Become irritable if you have been prohibited from using Facebook? .59
BFAS15 Felt bad if you, for dierent reasons, could not log on to Facebook
for some time? .58
Conict
BFAS16* Used Facebook so much that it has had a negative impact on your
job/studies? .66
BFAS17 Given less priority to hobbies, leisure activities, and exercise
because of Facebook? .60
BFAS18 Ignored your partner, family members, or friends because of
Facebook? .51
*Items retained in the nal model/scale. All items are scored on the following scale: 1: Very
rarely, 2: Rarely, 3: Sometimes, 4: Often, 5: Very often.

Bergen FaceBook addiction Scale 517
APPENDIX B
Means, standaRd deviations, and PeaRson coRRelation coeFFicients
FoR iteMs oF BeRgen FaceBook addiction scale (N = 405)
Scale Item M SD 5 7 11 13 16
11.99 1.02 .51 .34 .37 .47 .37
5 2.10 1.01 .48 .50 .54 .56
71.48 0.80 .43 .40 .38
11 1.54 0.95 .46 .51
13 1.65 0.85 .45
16 1.78 0.97